Chroma Vector Database
Orchestra-Research/AI-Research-SKILLs
Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.
当系统提示词需要定义 AI 如何分类用户意图、路由到不同处理流程、决定澄清策略和自主度级别时调用此 Skill。适用于多任务型 AI 助手、客服机器人、编程工具、研究助手等需要结构化对话管理的场景。不适用于：纯问答型系统（无任务执行）、单轮交互（无对话状态）、简单的 prompt 模板（无路由逻辑）。当需求仅涉及"输出什么格式"而非"如何决定输出什么"时，应该用…
Install Conversation Flow
$ npx skills add kangarooking/system-prompt-skills --skill conversation-flow -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install kangarooking/system-prompt-skills conversation-flow --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/conversation-flow .claude/skills/conversation-flow && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "conversation-flow" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/conversation-flow into .claude/skills/conversation-flow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conversation-flow", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/kangarooking/system-prompt-skills/tree/main/conversation-flowType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add kangarooking/system-prompt-skills --skill conversation-flow -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install kangarooking/system-prompt-skills conversation-flow --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/conversation-flow .agents/skills/conversation-flow && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "conversation-flow" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/conversation-flow into .agents/skills/conversation-flow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conversation-flow", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add kangarooking/system-prompt-skills --skill conversation-flow -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install kangarooking/system-prompt-skills conversation-flow --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/conversation-flow .cursor/skills/conversation-flow && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "conversation-flow" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/conversation-flow into .cursor/skills/conversation-flow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conversation-flow", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/kangarooking/system-prompt-skills.git --path conversation-flow--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add kangarooking/system-prompt-skills --skill conversation-flow -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install kangarooking/system-prompt-skills conversation-flow --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/conversation-flow .gemini/skills/conversation-flow && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "conversation-flow" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/conversation-flow into .gemini/skills/conversation-flow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conversation-flow", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install kangarooking/system-prompt-skills conversation-flowInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add kangarooking/system-prompt-skills --skill conversation-flow -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/conversation-flow .github/skills/conversation-flow && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "conversation-flow" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/conversation-flow into .github/skills/conversation-flow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conversation-flow", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add kangarooking/system-prompt-skills --skill conversation-flow -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install kangarooking/system-prompt-skills conversation-flow --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/conversation-flow .opencode/skills/conversation-flow && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "conversation-flow" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/conversation-flow into .opencode/skills/conversation-flow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conversation-flow", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
conversation-flow- GitHub stars
- 205
- Used in
- 1 other repo
- Token cost
- ~788 tokens
- SKILL.md length
- 206 words
- Files
- 1
- Skills in repo
- 15
- Repo updated
- First seen
- Licence
- MIT
At a glance
当系统提示词需要定义 AI 如何分类用户意图、路由到不同处理流程、决定澄清策略和自主度级别时调用此 Skill。适用于多任务型 AI 助手、客服机器人、编程工具、研究助手等需要结构化对话管理的场景。不适用于：纯问答型系统（无任务执行）、单轮交互（无对话状态）、简单的 prompt 模板（无路由逻辑）。当需求仅涉及"输出什么格式"而非"如何决定输出什么"时，应该用…
- Works in 7 steps: 意图二分法 —… → 领域路由表 —… → 澄清策略谱系 —… → …
- AI & LLM Engineering work in your project
- SKILL.md covers R — 原文 (Reading), I — 方法论骨架 (Interpretation), A1 — 案例分析 (Past Application) and A2 — 触发场景 (Future Trigger) ★, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Conversation Flow is an agent skill from kangarooking/system-prompt-skills. 当系统提示词需要定义 AI 如何分类用户意图、路由到不同处理流程、决定澄清策略和自主度级别时调用此 Skill。适用于多任务型 AI 助手、客服机器人、编程工具、研究助手等需要结构化对话管理的场景。不适用于：纯问答型系统（无任务执行）、单轮交互（无对话状态）、简单的 prompt 模板（无路由逻辑）。当需求仅涉及"输出什么格式"而非"如何决定输出什么"时，应该用 output-formatting 而非本 Skill。
Its SKILL.md is about 790 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering. It works with OpenAI. The repository describes itself as: 从 165 个顶级 AI 产品系统提示词中蒸馏出的 15 个可执行 Agent skill. The licence is MIT.
When your agent uses it
- AI & LLM Engineering work in your project
Example prompts
- “输出什么格式”
- “如何决定输出什么”
- “/conversation-flow”
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- 意图二分法 — 首先将用户输入分类为"信息查询"（问题）或"任务执行"（动作），触发不同处理管线
- 领域路由表 — 为每个已识别领域（邮件/文档/代码/搜索等）建立专用处理流程，含输入验证和输出格式
- 澄清策略谱系 — 从"先问再做"（高澄清）到"假设并继续"（低澄清），按任务复杂度和风险级别选择
- 自主度分级 — 定义 AI 在多大程度上可以自主推进：低（每步确认）→ 中（关键节点确认）→ 高（完成后汇报）
- 结构化工作流生命周期 — 提问 → 探索 → 规划 → 执行 → 验证 → 总结，每个阶段有明确的进入/退出条件
- 简化任务快速通道 — 对预估复杂度低于阈值的任务（如 Codex 的 25% 简单任务），跳过规划直接执行
- 工具优先原则 — 如果一个请求可以通过工具调用直接解决，立即调用工具，不请求许可（Notion AI）
What it can do on your machine
Read from SKILL.md and the folder at commit 252cd52. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Conversation Flow loads about 788 tokens when it runs. Until then it costs about 57 tokens; SKILL.md has 206 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from kangarooking/system-prompt-skills at commit 252cd52, republished under its MIT licence (© kangarooking). 206 words, ~788 tokens.
Download SKILL.mdSave it as
.claude/skills/conversation-flow/SKILL.md (or your agent's skills folder).- name
- conversation-flow
- description
- 当系统提示词需要定义 AI 如何分类用户意图、路由到不同处理流程、决定澄清策略和自主度级别时调用此 Skill。适用于多任务型 AI 助手、客服机器人、编程工具、研究助手等需要结构化对话管理的场景。不适用于：纯问答型系统（无任务执行）、单轮交互（无对话状态）、简单的 prompt 模板（无路由逻辑）。当需求仅涉及"输出什么格式"而非"如何决定输出什么"时，应该用 output-formatting 而非本 Skill。
- tags
- 对话路由, 意图分类, 澄清策略, 自主度控制, 工作流生命周期
- related_skills
- output-formatting, agent-delegation, context-management
对话流程与路由设计
R — 原文 (Reading)
跨供应商系统提示词中浮现的对话管理核心模式：先将用户输入二分为"问题"与"任务"（Warp），再按领域路由到专用处理流程（Claude Chrome 的"芯片"机制）。Claude Design 要求新设计至少提问 10 个问题才开工；ChatGPT Agent 则主张"尽可能推进，只在被阻塞时才请求澄清"。Codex 对简单任务跳过规划，Jules 有正式的计划评审步骤。核心张力在于"先问清楚"与"先做了再说"之间的平衡。
I — 方法论骨架 (Interpretation)
- 意图二分法 — 首先将用户输入分类为"信息查询"（问题）或"任务执行"（动作），触发不同处理管线
- 领域路由表 — 为每个已识别领域（邮件/文档/代码/搜索等）建立专用处理流程，含输入验证和输出格式
- 澄清策略谱系 — 从"先问再做"（高澄清）到"假设并继续"（低澄清），按任务复杂度和风险级别选择
- 自主度分级 — 定义 AI 在多大程度上可以自主推进：低（每步确认）→ 中（关键节点确认）→ 高（完成后汇报）
- 结构化工作流生命周期 — 提问 → 探索 → 规划 → 执行 → 验证 → 总结，每个阶段有明确的进入/退出条件
- 简化任务快速通道 — 对预估复杂度低于阈值的任务（如 Codex 的 25% 简单任务），跳过规划直接执行
- 工具优先原则 — 如果一个请求可以通过工具调用直接解决，立即调用工具，不请求许可（Notion AI）
A1 — 案例分析 (Past Application)
案例: Claude Design 的十问启动流程
- 问题: 设计任务的高度开放性导致 AI 经常基于模糊需求产出偏离用户期望的结果
- 设计模式的使用: Claude Design 系统提示词要求在开始任何新设计前至少提出 10 个澄清问题，涵盖目标用户、设计风格、功能范围、技术约束等维度。工作流为：提问 → 探索 → 规划 → 构建 → 验证 → 总结
- 结论: 强制澄清阶段虽然增加了前置轮次，但显著减少了后期返工率
案例: ChatGPT Agent 的"尽可能推进"策略
- 问题: 频繁请求用户确认导致任务完成效率低下，用户体验碎片化
- 设计模式的使用: ChatGPT Agent 系统提示词要求"尽可能推进，不做不必要的检查"。仅在真正被阻塞时才请求用户澄清，否则使用"假设...并继续"模式自主推进
- 结论: 该策略适合高自主度场景，但对模糊需求的容错率较低，需要在"推进速度"和"方向准确"之间权衡
案例: Warp 的二分路由
- 问题: 混合处理查询和任务导致输出格式混乱——回答问题时给出执行步骤，执行任务时给出理论解释
- 设计模式的使用: Warp 系统提示词将用户输入严格二分为"问题"（→ 简洁指令式回答）和"任务"（→ 直接执行），消除格式歧义
- 结论: 简单的二分路由显著提升了输出一致性，尤其适合终端/CLI 等高效场景
A2 — 触发场景 (Future Trigger) ★
用户在什么情境下需要?
- 设计多任务型 AI 助手，需要根据用户意图路由到不同处理流程（如客服机器人的工单/FAQ/人工转接）
- 构建编程/设计工具类 AI，需要在"先问清楚"和"先做再说"之间找到平衡点
- 优化现有 AI 产品的对话体验——用户反馈"问太多问题"或"不问就做，方向经常跑偏"
- 为 AI Agent 设计工作流生命周期，需要定义规划→执行→验证的标准流程
- 实现"快速通道"机制——简单任务跳过规划直接执行，复杂任务走完整流程
语言信号
- "AI 问的问题太多/太少了"
- "不同类型的请求需要不同的处理方式"
- "简单任务不要走复杂流程"
- "需要先规划再执行"
- "用户说了一句话就期望 AI 开始做事"
与相邻 skill 的区分
- 与
output-formatting的区别: output-formatting 控制输出的"形式"，本 Skill 控制决定"输出什么"的流程逻辑 - 与
agent-delegation的区别: agent-delegation 管理多代理间的任务分配，本 Skill 管理单代理内的对话路由 - 与
context-management的区别: context-management 管理信息存储和加载，本 Skill 管理对话决策逻辑
E — 可执行步骤 (Execution)
定义意图分类体系 — 完成标准: 建立至少三级意图分类（如：信息查询 / 简单任务 / 复杂任务），每级有明确的判断标准和示例输入
设计领域路由表 — 完成标准: 为每个支持的领域（至少 3 个）定义专用处理流程，包含输入验证规则、处理步骤、输出格式要求和异常处理路径
建立澄清策略谱系 — 完成标准: 定义至少三档澄清策略（高/中/低），每档明确触发条件（如任务风险级别、信息完整度评分），并给出每档的示例对话模式
设计工作流生命周期 — 完成标准: 定义至少五个阶段（提问→探索→规划→执行→验证），每阶段有明确的进入条件、核心动作、退出条件和可跳过条件
实现快速通道机制 — 完成标准: 定义简单任务的判定标准（如输入长度 < N 且含明确指令动词），以及快速通道的跳过规则（跳过哪些阶段、保留哪些检查点）
B — 边界 (Boundary) ★
不要在以下情况使用
- 单轮问答系统——没有对话状态需要管理，路由是多余的
- 纯 API 服务——请求-响应模式，没有对话流需要设计
- 规则非常固定的自动化流程——不需要 AI 自主决策路由
- 创意对话/闲聊场景——过度结构化会破坏自然感
常见失败模式
- 过度路由：为每种可能的输入都设计专用流程，导致路由表膨胀且难以维护
- 澄清不足：在"尽可能推进"策略下对模糊需求也直接执行，导致方向错误和大量返工
- 澄清过度：在简单任务上强制走完整提问流程，用户体验像填表而非对话
- 快速通道误判：将复杂任务误判为简单任务跳过规划，导致执行失败后不得不回滚
- 忽视上下文连续性：每次输入都重新分类，丢失对话历史中的意图信息
© kangarooking, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in conversation-flow of kangarooking/system-prompt-skills.
Open the folder on GitHubat commit 252cd52
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in kangarooking/system-prompt-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Conversation Flow next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Conversation Flow this skillkangarooking/system-prompt-skills | 205 | 1 repos | ~788 | Automated safety check: Pass | MIT | |
| Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~2.3k | Automated safety check: Pass | MIT | |
| CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Codebase Managementgiancarloerra/SocratiCode | 3.3k | 1 repos | ~1.8k | Automated safety check: Pass | AGPL-3.0 | |
| Azure AI Projects Python SDKmicrosoft/skills | 3.1k | 6 repos | ~2.8k | Automated safety check: Pass | MIT | |
| Fine-Tuning ExpertJeffallan/claude-skills | 12k | 1 repos | ~1.7k | Automated safety check: Pass | MIT |
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microsoft/skills
Reference for building on Microsoft Foundry with the azure-ai-projects Python SDK: project clients, versioned agents, evaluations, connections, datasets and indexes.
Fine-Tuning Expert
Jeffallan/claude-skills
Guides LLM fine-tuning with LoRA and QLoRA through Hugging Face PEFT, from dataset validation and training checks to adapter merging, quantization and deployment.
Docs Planner
strands-agents/harness-sdk
Identify documentation gaps and prioritize the docs backlog.
More from kangarooking/system-prompt-skills
All 15 skills in this repoPersona Design
kangarooking/system-prompt-skills
当需要为 AI 产品定义核心身份、角色声明和能力边界时调用此 skill。典型场景包括：设计新 AI 产品的 system prompt 首段、为不同场景创建差异化角色（如教学助手 vs 编程代理）、重新定义 AI 与用户的关系框架。
Tool Specification
kangarooking/system-prompt-skills
当需要为 AI 定义工具接口、设计调用规范、实现工具发现与编排机制时调用此 skill。典型场景包括：设计 AI agent 的工具集、定义 JSON Schema/XML/TypeScript 格式的工具描述、实现工具权限控制与并行调度、设计子代理委托架构。
Memory System
kangarooking/system-prompt-skills
当需要为 AI 设计记忆存储、检索、应用和更新机制时调用此 skill。典型场景包括：设计持久化记忆架构（用户偏好、历史上下文、项目知识）、定义记忆的创建/读取/更新/删除生命周期、实现静默记忆应用（不在回复中透露记忆内容）、管理敏感记忆边界。
Personality System
kangarooking/system-prompt-skills
当需要在基础身份之上叠加可切换的人格风格层时调用此 skill。典型场景包括：为同一产品提供多种人格选项（如 GPT-5.1 的 friendly/professional/quirky 模式）、设计人格切换机制、防止人格泄露到用户内容中。
Safety Guardrails
kangarooking/system-prompt-skills
当需要为 AI 系统设计多层安全防线、内容过滤策略和伦理边界时调用此 skill。典型场景包括：设计拒绝策略与升级机制、防御 prompt 注入攻击、实现领域特定安全规则（教育、医疗、金融等）、定义 AI 的价值观锚点。
Agent Delegation
kangarooking/system-prompt-skills
当系统提示词需要设计多代理协作架构、子代理专业化分工、代理间上下文隔离与传递机制、任务生命周期管理时调用此 Skill。适用于 AI Agent 平台、多工具编排系统、代码审查流水线、跨应用协作场景等。不适用于：单代理系统（无委派需求）、简单工具调用（无子代理概念）、纯 API 编排（无 AI 决策）。当需求聚焦于"单代理内的对话路由"而非"多代理间的任务分配"时，应该用…
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Questions about Conversation Flow
What does Conversation Flow do?
当系统提示词需要定义 AI 如何分类用户意图、路由到不同处理流程、决定澄清策略和自主度级别时调用此 Skill。适用于多任务型 AI 助手、客服机器人、编程工具、研究助手等需要结构化对话管理的场景。不适用于：纯问答型系统（无任务执行）、单轮交互（无对话状态）、简单的 prompt 模板（无路由逻辑）。当需求仅涉及"输出什么格式"而非"如何决定输出什么"时，应该用…. Conversation Flow is an agent skill from kangarooking/system-prompt-skills.
When should I use Conversation Flow?
Conversation Flow fits situations like: AI & LLM Engineering work in your project.
How do I install Conversation Flow in Claude Code?
Run `npx skills add kangarooking/system-prompt-skills --skill conversation-flow -a claude-code`. Or copy the skill folder (conversation-flow in kangarooking/system-prompt-skills) into .claude/skills/conversation-flow in your project. Claude Code loads it when a task matches its description.
How do I install Conversation Flow in Codex?
Run `npx skills add kangarooking/system-prompt-skills --skill conversation-flow -a codex`. Or copy the skill folder (conversation-flow in kangarooking/system-prompt-skills) into .agents/skills/conversation-flow in your project. Codex loads it when a task matches its description.
Can I use Conversation Flow in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add kangarooking/system-prompt-skills --skill conversation-flow -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/conversation-flow, .gemini/skills/conversation-flow, .github/skills/conversation-flow and .opencode/skills/conversation-flow in your project.
What does Conversation Flow need to run?
SKILL.md names no scripts, command-line tools or credentials: Conversation Flow is instructions for the agent only.
Does Conversation Flow access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Conversation Flow safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Conversation Flow use?
Conversation Flow is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Conversation Flow use?
About 788 tokens (SKILL.md is roughly 3.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Conversation Flow?
Skills that share tags, products or a category with Conversation Flow: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars), Codebase Management (giancarloerra/SocratiCode, 3.3k stars) and Azure AI Projects Python SDK (microsoft/skills, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Conversation Flow?
kangarooking (a GitHub user) maintains it in kangarooking/system-prompt-skills, which has 205 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on May 4, 2026.
Source: kangarooking/system-prompt-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.